mirrors
/
deep-person-reid
mirror of https://github.com/KaiyangZhou/deep-person-reid.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity
deep-person-reid/_modules/torchreid/models/mudeep.html

415 lines
45 KiB
HTML
Raw Blame History

<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>torchreid.models.mudeep &mdash; torchreid 0.7.2 documentation</title>
<script type="text/javascript" src="../../../_static/js/modernizr.min.js"></script>
<script type="text/javascript" id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
<script type="text/javascript" src="../../../_static/jquery.js"></script>
<script type="text/javascript" src="../../../_static/underscore.js"></script>
<script type="text/javascript" src="../../../_static/doctools.js"></script>
<script type="text/javascript" src="../../../_static/language_data.js"></script>
<script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
<script type="text/javascript" src="../../../_static/js/theme.js"></script>
<link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
<link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
<link rel="index" title="Index" href="../../../genindex.html" />
<link rel="search" title="Search" href="../../../search.html" />
</head>
<body class="wy-body-for-nav">
<div class="wy-grid-for-nav">
<nav data-toggle="wy-nav-shift" class="wy-nav-side">
<div class="wy-side-scroll">
<div class="wy-side-nav-search" >
<a href="../../../index.html" class="icon icon-home"> torchreid
</a>
<div class="version">
0.7.2
</div>
<div role="search">
<form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
<input type="text" name="q" placeholder="Search docs" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div>
<div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide.html">How-to</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../datasets.html">Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../evaluation.html">Evaluation</a></li>
</ul>
<p class="caption"><span class="caption-text">Package Reference</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../pkg/data.html">torchreid.data</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../pkg/engine.html">torchreid.engine</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../pkg/losses.html">torchreid.losses</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../pkg/metrics.html">torchreid.metrics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../pkg/models.html">torchreid.models</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../pkg/optim.html">torchreid.optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../pkg/utils.html">torchreid.utils</a></li>
</ul>
<p class="caption"><span class="caption-text">Resources</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../AWESOME_REID.html">Awesome-ReID</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../MODEL_ZOO.html">Model Zoo</a></li>
</ul>
</div>
</div>
</nav>
<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
<nav class="wy-nav-top" aria-label="top navigation">
<i data-toggle="wy-nav-top" class="fa fa-bars"></i>
<a href="../../../index.html">torchreid</a>
</nav>
<div class="wy-nav-content">
<div class="rst-content">
<div role="navigation" aria-label="breadcrumbs navigation">
<ul class="wy-breadcrumbs">
<li><a href="../../../index.html">Docs</a> &raquo;</li>
<li><a href="../../index.html">Module code</a> &raquo;</li>
<li>torchreid.models.mudeep</li>
<li class="wy-breadcrumbs-aside">
</li>
</ul>
<hr/>
</div>
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div itemprop="articleBody">
<h1>Source code for torchreid.models.mudeep</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">division</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;MuDeep&#39;</span><span class="p">]</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">nn</span>
<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="k">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="k">class</span> <span class="nc">ConvBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Basic convolutional block.</span>
<span class="sd"> </span>
<span class="sd"> convolution + batch normalization + relu.</span>
<span class="sd"> Args:</span>
<span class="sd"> in_c (int): number of input channels.</span>
<span class="sd"> out_c (int): number of output channels.</span>
<span class="sd"> k (int or tuple): kernel size.</span>
<span class="sd"> s (int or tuple): stride.</span>
<span class="sd"> p (int or tuple): padding.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_c</span><span class="p">,</span> <span class="n">out_c</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ConvBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_c</span><span class="p">,</span> <span class="n">out_c</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">p</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_c</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bn</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
<span class="k">class</span> <span class="nc">ConvLayers</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Preprocessing layers.&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ConvLayers</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">ConvBlock</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">ConvBlock</span><span class="p">(</span><span class="mi">48</span><span class="p">,</span> <span class="mi">96</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">maxpool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">maxpool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">class</span> <span class="nc">MultiScaleA</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Multi-scale stream layer A (Sec.3.1)&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MultiScaleA</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stream1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">96</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stream2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stream3</span> <span class="o">=</span> <span class="n">ConvBlock</span><span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stream4</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">24</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">s1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stream1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">s2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stream2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">s3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stream3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">s4</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stream4</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">,</span> <span class="n">s3</span><span class="p">,</span> <span class="n">s4</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">y</span>
<span class="k">class</span> <span class="nc">Reduction</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Reduction layer (Sec.3.1)&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Reduction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stream1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stream2</span> <span class="o">=</span> <span class="n">ConvBlock</span><span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">96</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stream3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">48</span><span class="p">,</span> <span class="mi">56</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">56</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">s1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stream1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">s2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stream2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">s3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stream3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">,</span> <span class="n">s3</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">y</span>
<span class="k">class</span> <span class="nc">MultiScaleB</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Multi-scale stream layer B (Sec.3.1)&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MultiScaleB</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stream1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stream2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stream3</span> <span class="o">=</span> <span class="n">ConvBlock</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stream4</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
<span class="n">ConvBlock</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">s1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stream1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">s2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stream2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">s3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stream3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">s4</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stream4</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">,</span> <span class="n">s3</span><span class="p">,</span> <span class="n">s4</span>
<span class="k">class</span> <span class="nc">Fusion</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Saliency-based learning fusion layer (Sec.3.2)&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Fusion</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">a1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">a2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">a3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">a4</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="c1"># We add an average pooling layer to reduce the spatial dimension</span>
<span class="c1"># of feature maps, which differs from the original paper.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">avgpool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="n">x3</span><span class="p">,</span> <span class="n">x4</span><span class="p">):</span>
<span class="n">s1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a1</span><span class="o">.</span><span class="n">expand_as</span><span class="p">(</span><span class="n">x1</span><span class="p">)</span> <span class="o">*</span> <span class="n">x1</span>
<span class="n">s2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a2</span><span class="o">.</span><span class="n">expand_as</span><span class="p">(</span><span class="n">x2</span><span class="p">)</span> <span class="o">*</span> <span class="n">x2</span>
<span class="n">s3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a3</span><span class="o">.</span><span class="n">expand_as</span><span class="p">(</span><span class="n">x3</span><span class="p">)</span> <span class="o">*</span> <span class="n">x3</span>
<span class="n">s4</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a4</span><span class="o">.</span><span class="n">expand_as</span><span class="p">(</span><span class="n">x4</span><span class="p">)</span> <span class="o">*</span> <span class="n">x4</span>
<span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">avgpool</span><span class="p">(</span><span class="n">s1</span> <span class="o">+</span> <span class="n">s2</span> <span class="o">+</span> <span class="n">s3</span> <span class="o">+</span> <span class="n">s4</span><span class="p">)</span>
<span class="k">return</span> <span class="n">y</span>
<div class="viewcode-block" id="MuDeep"><a class="viewcode-back" href="../../../pkg/models.html#torchreid.models.mudeep.MuDeep">[docs]</a><span class="k">class</span> <span class="nc">MuDeep</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Multiscale deep neural network.</span>
<span class="sd"> Reference:</span>
<span class="sd"> Qian et al. Multi-scale Deep Learning Architectures</span>
<span class="sd"> for Person Re-identification. ICCV 2017.</span>
<span class="sd"> Public keys:</span>
<span class="sd"> - ``mudeep``: Multiscale deep neural network.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MuDeep</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span>
<span class="bp">self</span><span class="o">.</span><span class="n">block1</span> <span class="o">=</span> <span class="n">ConvLayers</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">block2</span> <span class="o">=</span> <span class="n">MultiScaleA</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">block3</span> <span class="o">=</span> <span class="n">Reduction</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">block4</span> <span class="o">=</span> <span class="n">MultiScaleB</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">block5</span> <span class="o">=</span> <span class="n">Fusion</span><span class="p">()</span>
<span class="c1"># Due to this fully connected layer, input image has to be fixed</span>
<span class="c1"># in shape, i.e. (3, 256, 128), such that the last convolutional feature</span>
<span class="c1"># maps are of shape (256, 16, 8). If input shape is changed,</span>
<span class="c1"># the input dimension of this layer has to be changed accordingly.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">256</span><span class="o">*</span><span class="mi">16</span><span class="o">*</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4096</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="mi">4096</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4096</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">feat_dim</span> <span class="o">=</span> <span class="mi">4096</span>
<span class="k">def</span> <span class="nf">featuremaps</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">block1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">block2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">block3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">block4</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">block5</span><span class="p">(</span><span class="o">*</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">featuremaps</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">==</span> <span class="s1">&#39;softmax&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">y</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">==</span> <span class="s1">&#39;triplet&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">y</span><span class="p">,</span> <span class="n">x</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s1">&#39;Unsupported loss: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">))</span></div>
</pre></div>
</div>
</div>
<footer>
<hr/>
<div role="contentinfo">
<p>
&copy; Copyright 2019, Kaiyang Zhou
</p>
</div>
Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
</footer>
</div>
</div>
</section>
</div>
<script type="text/javascript">
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
});
</script>
</body>
</html>
Reference in New Issue View Git Blame Copy Permalink